Development of SIT Hybrid Machine Learning Algorithm for Hour Level Building Energy Consumption Probabilistic Prediction

Yiping Meng¹, Yiming Sun², Sergio Rodriguez¹, Farzad Pour Rahimian¹

1. Teesside University
2. University of Sheffield

Published in Proceedings of the International Conference on Smart and Sustainable Built Environment (SASBE 2024), edited by Ali GhaffarianHoseini, Amirhosein GhaffarianHoseini, Farzad Rahimian and Mahesh Babu Purushothaman. Springer Nature, Lecture Notes in Civil Engineering, volume 591, 2025, pages 629 to 638. DOI 10.1007/978-981-96-4051-5_61.

Abstract

With the building sector accounting for 40% of global energy consumption, achieving Net Zero by 2050 emerges as a paramount challenge, necessitating precise energy consumption forecasting to streamline smart energy supply chains amidst unforeseen uncertainties. Traditional prediction models often falter in navigating the intricate, non-linear interplay of factors such as climate, thermal system performance, and occupancy behaviours, creating a critical research gap. This study introduces a groundbreaking hybrid machine-learning algorithm that synergizes the sparse, interpretable, and transparent (SIT) nature of the NARMAX model’s advanced temporal sequence processing capabilities. Employing the REFIT Smart Home dataset, which provides two years of hourly resolution data, our methodology showcases remarkable precision in probabilistic energy consumption forecasting. A comparative analysis underscores the proposed hybrid model’s superiority over established methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), particularly in reducing Root Mean Squared Error (RMSE) and improving the Coefficient of Variation (CV). This innovative fusion not only bridges the existing precision-interpretability gap but also paves the way for more efficient, predictive energy management frameworks in the building sector.

Keywords

Session

Presented in Recorded Presentations, Session IV, Saturday 9 November 2024, 16:15 to 18:15, room WG 201, Auckland University of Technology. Session chair Dr Dat Doan.

How to Cite

Meng, Y., Sun, Y., Rodriguez, S., & Rahimian, F. P. (2025). Development of SIT Hybrid Machine Learning Algorithm for Hour Level Building Energy Consumption Probabilistic Prediction. In A. GhaffarianHoseini, A. GhaffarianHoseini, F. Rahimian, & M. B. Purushothaman (Eds.), Proceedings of the International Conference on Smart and Sustainable Built Environment (SASBE 2024) (Lecture Notes in Civil Engineering, Vol. 591, pp. 629–638). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-4051-5_61

About the Conference

Presented at SASBE 2024, the International Conference on Smart and Sustainable Built Environment, held in Auckland from 7 to 9 November 2024 and chaired by Professors Ali and Amirhosein GhaffarianHoseini, founders of GDI Academy. The version of record is published by Springer Nature; this page is the conference archive record kept by GDI Academy.